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Transcript of SENTIMENT ANALYSIS
The web today and the importance of analysis
SA & OP !!!
Using NLP, statistics, or machine learning methods to extract, identify, or otherwise characterize the sentiment content of a collection of text.
THE WEB TODAY
THE NEED FOR ANALYSIS
STATE OF ART
1. NEGATION- DOUBLE & BLIND
3. ACRONYMS , JARGONS AND EMOTICONS
State of Art
Marketing tool: extracted sentiment may be used to predict future trends and behavior.
Longitudinal Analysis: Where are the cycles and patterns in sentiment?
Root cause analysis.
The ability to interpret human emotions.
Enables advances in NLP
Improvements in machine learning accuracy.
FUTURE: Predictive analysis & Analysis of non-textual input
1. Language Issues
2. Context Issues
3. Domain Issues
4. Subjectivity Issues
1. Data Volume
2. System Nature
1. data volume: ML METHODs NOT too SCALABLE
2. system nature: sandbox environment
1. implement: resource extraction; training time
2. TEST: REAL DATA
1. DOUBLE MEANING
2. multiple OPINION
POSITIVE OR NEG?
2. ASPECT IMPORTANCE
1. aCTUAL RESULT COULD CONTAIN DUAL EMOtiONS.
2. every opinion is subjective and complex for even human
3. sometimes texts are gramatically or linguistically
4. stength of opinion not clearly understood.
An intensity based result following a rule based system providing information on all aspects of a phrase is found to be more accurate than a general lexicon or machine learning based method.
This system giving deeper insights also took lesser time and performed better.
However, a machine learning outlook on this would be more useful for future works. For this a huge training data set that is cross-domain and culturally&linguistically varied text would be appreciated.
A more NLP approach that handles dependencies between the nature of the words may also be considered.
In conclusion, this research has high potential for further development.
A combination of keyword based and NLP techniques may be combined to give better results.
Check the nature of each word and also the nature of the term before and after it.
Taking into consideration the informal nature of the text on social media:
-Deliberate spelling errors
Computing each polarity aspect- because that's how human brains compute.
Also check the compound nature, i.e. if two or more opinions are involved.
Data is the core of any data mining problem.
The importance of meaningful data is under-stated.
It's important to use good data in 4 stages:
-Dictionary or the lexical resource used
-The data used to train on
-The data used to test on
-The data used to implement on
Different requirements for each stage.
Importance of processing and normalizing but not losing out on useful info.
Twitter: rich, open source of data.
Use it to identify emoticons and tech jargons: empirical scoring.
Adjectives:ffective Norms for English Words (ANEW- AFINN)
e.g. The character was written well and was beautiful.
5-7 sec less than ML
so much data!
expert & naive opinions
140 char only!